Efficient Machine Learning-Assisted Multiobjective Antenna Design for Internet-of-Things Applications

In this letter, an efficient machine learning framework tailored for multiobjective antenna design is proposed. A hybrid neural network is designed as the decoder for training, possessing the ability to automatically determine the optimal number of network layers. This adaptability vastly speeds up electromagnetic (EM) response generation for antennas, eliminating the need for intensive EM simulations. Subsequently, encoders, mapping networks, and pretrained decoders are sequentially connected to train the encoders and mapping network, with the purpose of learning features associated with multiple metrics. To this end, when the multiobjective changes, our proposed framework has the merit of facilitating swift retraining of the neural network. Simulation results of an split-ring resonator (SRR)-loaded patch antenna and an 8-element multiple-input–multiple-output antenna, both for Internet-of-Things (IoT) terminals, are provided to verify the proposed framework. The two antennas, respectively, achieve bandwidths of 6 GHz (21.20 GHz to 27.20 GHz) and 2.76 GHz (3.26 GHz to 6.02 GHz). Numerical results reveal the superiority and effectiveness of the proposed framework.